Learn how to detect network anomalies using the Azure machine learning service by running a sample binary classification experiment.
- [Instructor] Let's use Azure Machine Learning Service…to detect network anomalies.…The data set we are using here is KDD Cup 1999.…KDD stands for Knowledge Discovery and Data Mining…and is the name of a conference.…KDD Cup 1999 was a competition…to build a network intrusion detector.…The data set consists of a purest form…of captured network packets with their various features…to be processed by an algorithm.…
In this exercise, we're using a sample experiment…created by the Microsoft Azure Machine Learning team.…The goal of the experiment is…to analyze the KDD Cup 1999 data…and predict which network transaction is malicious.…In our experiment, we consider only two possibilities:…malicious transaction that is an intrusion attempt,…versus normal transaction.…Which is why the experiment is called binary classification.…
Now let's load the experiment…into my Microsoft Machine Learning Studio account.…So I click on Open Studio…and just click on the check mark,…and click on okay.…Now the experiment has been loaded.…
- Identify the goals of network security.
- Distinguish types of firewalls.
- Explain intrusion detection and prevention systems.
- Describe packet capture.
- Collect packet sniffer, IDS, and IPS data.
- Explain how to use machine learning to process network data.
- Use data science to conduct a network forensics investigation.
- Identify data visualization targets and tools.
Skill Level Intermediate
1. Network Security Review
2. Network Data Sources
3. Data Collection
4. Data Analytics
Network forensics2m 25s
- Mark as unwatched
- Mark all as unwatched
Are you sure you want to mark all the videos in this course as unwatched?
This will not affect your course history, your reports, or your certificates of completion for this course.Cancel
Take notes with your new membership!
Type in the entry box, then click Enter to save your note.
1:30Press on any video thumbnail to jump immediately to the timecode shown.
Notes are saved with you account but can also be exported as plain text, MS Word, PDF, Google Doc, or Evernote.